Annotate tumoral cells
2021/04/09
1 Introduction
The purpose of this notebook is to find markers for each of the clusters defined previously, so that we can annotate them to specific clones or cell types.
2 Pre-processing
2.1 Load packages
library(Seurat)
library(SeuratWrappers)
library(harmony)
library(tidyverse)2.2 Define parameters
# Paths
path_to_obj <- here::here("results/R_objects/5.seurat_list_clustered.rds")
path_to_save <- here::here("results/R_objects/6.seurat_list_annotated.rds")
# Colors
color_palette <- c("black", "gray", "red", "yellow", "violet", "green4",
"blue", "mediumorchid2", "coral2", "blueviolet",
"indianred4", "deepskyblue1", "dimgray", "deeppink1",
"green", "lightgray", "hotpink1")
# Source functions
source(here::here("bin/utils.R"))2.3 Load data
seurat_list <- readRDS(path_to_obj)
purrr::map(seurat_list, DimPlot)## $`12`
##
## $`19`
##
## $`3299`
purrr::map(seurat_list, DimPlot, group.by = "is_richter")## $`12`
##
## $`19`
##
## $`3299`
3 Cell Cycle Score
As upregulation of cell cycle genes is a hallmark of Richter transformation, we will infer the cell cycle score and phase for each cell:
seurat_list <- purrr::map(seurat_list, function(seurat_obj) {
seurat_obj <- CellCycleScoring(
seurat_obj,
s.features = cc.genes.updated.2019$s.genes,
g2m.features = cc.genes.updated.2019$g2m.genes,
set.ident = FALSE
)
seurat_obj
})
cc_phase_ggs <- purrr::map(seurat_list, DimPlot, group.by = "Phase")
s_score_ggs <- purrr::map(seurat_list, function(seurat_obj) {
p <- FeaturePlot(seurat_obj, features = "S.Score") +
scale_color_viridis_c(option = "magma")
p
})
g2m_score_ggs <- purrr::map(seurat_list, function(seurat_obj) {
p <- FeaturePlot(seurat_obj, features = "G2M.Score") +
scale_color_viridis_c(option = "magma")
p
})
cc_phase_ggs## $`12`
##
## $`19`
##
## $`3299`
s_score_ggs## $`12`
##
## $`19`
##
## $`3299`
g2m_score_ggs## $`12`
##
## $`19`
##
## $`3299`
4 Find Markers
markers_list <- purrr::map(
seurat_list,
FindAllMarkers,
only.pos = TRUE,
logfc.threshold = 0.5
)
markers_list <- purrr::map(markers_list, function(df) {
df <- df %>%
group_by(cluster) %>%
arrange(desc(avg_log2FC), .by_group = TRUE)
df
})
print("Markers 012")## [1] "Markers 012"DT::datatable(markers_list$`12`)print("Markers 019")## [1] "Markers 019"DT::datatable(markers_list$`19`)print("Markers 3299")## [1] "Markers 3299"DT::datatable(markers_list$`3299`)saveRDS(
markers_list,
here::here("3-clustering_and_annotation/tmp/markers_richter.rds")
)
# Split into lists of data.frames
markers_dfs_list <- list()
for (patient in names(markers_list)) {
clusters <- unique(markers_list[[patient]]$cluster)
markers_dfs <- purrr::map(clusters, function(x) {
df <- markers_list[[patient]][markers_list[[patient]]$cluster == x, ]
df <- df[, c(7, 1:6)]
df
})
names(markers_dfs) <- clusters
markers_dfs_list[[patient]] <- markers_dfs
}We can also compare all Richter-like clones against CLL:
markers_richter_12 <- FindMarkers(
seurat_list$`12`,
ident.1 = c("2", "3"),
ident.2 = c("0", "1"),
only.pos = FALSE,
logfc.threshold = 0.5
)
DT::datatable(markers_richter_12)markers_richter_19 <- FindMarkers(
seurat_list$`19`,
ident.1 = c("0", "1"),
ident.2 = c("2"),
only.pos = FALSE,
logfc.threshold = 0.5
)
DT::datatable(markers_richter_19)markers_richter_3299 <- FindMarkers(
seurat_list$`3299`,
ident.1 = "2",
ident.2 = c("0", "1"),
only.pos = FALSE,
logfc.threshold = 0.5
)
DT::datatable(markers_richter_3299)Interesting observations:
- H2AFY
- MIF
- LILRA4
- TCL1A
- PRDM2
- MIR155HG
- INPP5D
- XBP1
- CLPTM1L
- AC007952.4
- AC245014.3
5 Annotation
5.1 Patient 012
| Cluster | Markers | Annotation |
|---|---|---|
| 0 | CXCR4 | CXCR4+CD27- |
| 1 | CD27 | CXCR4-CD27+ |
| 2 | MIF | Richter-like quiescent |
| 3 | TOP2A, MKI67 | Richter-like proliferative |
| 4 | MT-ND1, MT-CO3 | poor-quality |
| 5 | MZB1, IGHM, XBP1, CLPTM1L | MZB1+IGHM++XBP1+ |
| 6 | HBM | erythrocytes |
annotation_012 <- c("CXCR4+CD27-", "CXCR4-CD27+", "Richter-like quiescent",
"Richter-like proliferative", "poor-quality",
"MZB1+IGHM++XBP1+", "erythrocytes")
seurat_list$`12`$seurat_clusters <- Idents(seurat_list$`12`)
seurat_list$`12`$annotation <- Idents(seurat_list$`12`)
levels(seurat_list$`12`$annotation) <- annotation_012
Idents(seurat_list$`12`) <- "annotation"
DimPlot(seurat_list$`12`, cols = color_palette)
5.2 Patient 019
| Cluster | Markers | Annotation |
|---|---|---|
| 0 | CXCR4 | CXCR4+CD27- |
| 1 | CD27 | CXCR4-CD27+ |
| 2 | CCR7 | Richter-like quiescent |
| 3 | MALAT | poor-quality |
| 4 | TOP2A, MKI67 | Richter-like proliferative |
| 5 | HBM | erythrocytes |
annotation_019 <- c("CXCR4+CD27-", "CXCR4-CD27+", "Richter-like quiescent",
"poor-quality", "Richter-like proliferative", "erythrocytes")
seurat_list$`19`$seurat_clusters <- Idents(seurat_list$`19`)
seurat_list$`19`$annotation <- Idents(seurat_list$`19`)
levels(seurat_list$`19`$annotation) <- annotation_019
Idents(seurat_list$`19`) <- "annotation"
DimPlot(seurat_list$`19`, cols = color_palette)
5.3 Patient 3299
| Cluster | Markers | Annotation |
|---|---|---|
| 0 | CXCR4 | CXCR4+CD27- |
| 1 | CD27 | CXCR4-CD27+ |
| 2 | KLF6 | Richter-like quiescent |
| 3 | mitochondrial genes | poor-quality |
| 4 | MIR155HG | MIR155HG |
| 5 | HBM/TOP2A | erythroblast |
annotation_3299 <- c("CXCR4+CD27-", "CXCR4-CD27+", "Richter-like quiescent",
"poor-quality", "MIR155HG", "erythroblast")
seurat_list$`3299`$seurat_clusters <- Idents(seurat_list$`3299`)
seurat_list$`3299`$annotation <- Idents(seurat_list$`3299`)
levels(seurat_list$`3299`$annotation) <- annotation_3299
Idents(seurat_list$`3299`) <- "annotation"
DimPlot(seurat_list$`3299`, cols = color_palette)
In all 3 donors we have seen a a cluster of poor-quality cells, but that has a high expression of important CLL genes: CHD2 and ATM, among others. Thus, we need to check carefully before excluding them.
6 Save
saveRDS(seurat_list, path_to_save)
# Save markers
walk2(markers_dfs_list, names(markers_dfs_list), function(dfs, x) {
path_to_save_dfs <- str_c(
here::here("results/tables/markers/"),
"markers_clusters_patient_",
x,
".xlsx"
)
openxlsx::write.xlsx(dfs, path_to_save_dfs)
})7 Session Information
sessionInfo()## R version 4.0.4 (2021-02-15)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=es_ES.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=es_ES.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=es_ES.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.6 purrr_0.3.4 readr_1.4.0 tidyr_1.1.3 tibble_3.1.2 ggplot2_3.3.3 tidyverse_1.3.0 harmony_1.0 Rcpp_1.0.6 SeuratWrappers_0.3.0 SeuratObject_4.0.1 Seurat_4.0.1 BiocStyle_2.18.1
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.2.1 plyr_1.8.6 igraph_1.2.6 lazyeval_0.2.2 splines_4.0.4 crosstalk_1.1.1 listenv_0.8.0 scattermore_0.7 digest_0.6.27 htmltools_0.5.1.1 fansi_0.4.2 magrittr_2.0.1 tensor_1.5 cluster_2.1.1 ROCR_1.0-11 openxlsx_4.2.3 limma_3.46.0 remotes_2.2.0 globals_0.14.0 modelr_0.1.8 matrixStats_0.58.0 spatstat.sparse_2.0-0 colorspace_2.0-1 rvest_1.0.0 ggrepel_0.9.1 haven_2.3.1 xfun_0.22 crayon_1.4.1 jsonlite_1.7.2 spatstat.data_2.1-0 survival_3.2-10 zoo_1.8-9 glue_1.4.2 polyclip_1.10-0 gtable_0.3.0 leiden_0.3.7 future.apply_1.7.0 abind_1.4-5 scales_1.1.1 DBI_1.1.1 miniUI_0.1.1.1 viridisLite_0.4.0 xtable_1.8-4 reticulate_1.20 spatstat.core_2.1-2 rsvd_1.0.3 DT_0.17 htmlwidgets_1.5.3 httr_1.4.2 RColorBrewer_1.1-2 ellipsis_0.3.2 ica_1.0-2 farver_2.1.0
## [55] pkgconfig_2.0.3 sass_0.4.0 uwot_0.1.10 dbplyr_2.1.0 deldir_0.2-10 here_1.0.1 utf8_1.2.1 labeling_0.4.2 tidyselect_1.1.1 rlang_0.4.11 reshape2_1.4.4 later_1.2.0 munsell_0.5.0 cellranger_1.1.0 tools_4.0.4 cli_2.5.0 generics_0.1.0 broom_0.7.5 ggridges_0.5.3 evaluate_0.14 fastmap_1.1.0 yaml_2.2.1 goftest_1.2-2 knitr_1.31 fs_1.5.0 fitdistrplus_1.1-3 zip_2.1.1 RANN_2.6.1 pbapply_1.4-3 future_1.21.0 nlme_3.1-152 mime_0.10 xml2_1.3.2 compiler_4.0.4 rstudioapi_0.13 plotly_4.9.3 png_0.1-7 spatstat.utils_2.1-0 reprex_1.0.0 bslib_0.2.5 stringi_1.6.2 highr_0.8 lattice_0.20-41 Matrix_1.3-3 vctrs_0.3.8 pillar_1.6.1 lifecycle_1.0.0 BiocManager_1.30.10 spatstat.geom_2.1-0 lmtest_0.9-38 jquerylib_0.1.4 RcppAnnoy_0.0.18 data.table_1.14.0 cowplot_1.1.1
## [109] irlba_2.3.3 httpuv_1.6.1 patchwork_1.1.1 R6_2.5.0 bookdown_0.21 promises_1.2.0.1 KernSmooth_2.23-18 gridExtra_2.3 parallelly_1.25.0 codetools_0.2-18 MASS_7.3-53.1 assertthat_0.2.1 rprojroot_2.0.2 withr_2.4.2 sctransform_0.3.2 mgcv_1.8-34 parallel_4.0.4 hms_1.0.0 grid_4.0.4 rpart_4.1-15 rmarkdown_2.7 Rtsne_0.15 shiny_1.6.0 lubridate_1.7.10